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» Supervised feature selection via dependence estimation
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IJCAI
2007
13 years 8 months ago
Simple Training of Dependency Parsers via Structured Boosting
Recently, significant progress has been made on learning structured predictors via coordinated training algorithms such as conditional random fields and maximum margin Markov ne...
Qin Iris Wang, Dekang Lin, Dale Schuurmans
PAMI
2006
206views more  PAMI 2006»
13 years 7 months ago
MILES: Multiple-Instance Learning via Embedded Instance Selection
Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called...
Yixin Chen, Jinbo Bi, James Ze Wang
ICCV
2011
IEEE
12 years 7 months ago
Informative Feature Selection for Object Recognition via Sparse PCA
Bag-of-words (BoW) methods are a popular class of object recognition methods that use image features (e.g., SIFT) to form visual dictionaries and subsequent histogram vectors to r...
Nikhil Naikal, Allen Y. Yang, S. Shankar Sastry
SSD
2005
Springer
122views Database» more  SSD 2005»
14 years 26 days ago
Selectivity Estimation of High Dimensional Window Queries via Clustering
Abstract. Query optimization is an important functionality of modern database systems and often based on estimating the selectivity of queries before actually executing them. Well-...
Christian Böhm, Hans-Peter Kriegel, Peer Kr&o...
ACL
2012
11 years 9 months ago
Selective Sharing for Multilingual Dependency Parsing
We present a novel algorithm for multilingual dependency parsing that uses annotations from a diverse set of source languages to parse a new unannotated language. Our motivation i...
Tahira Naseem, Regina Barzilay, Amir Globerson